A model for how correlation depends on the neuronal excitability type (Hong et al. 2012)

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“ … Using simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization. Contrary to rate comodulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently. The type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: “ideal” integrators (with spike generation sensitive to stimulus mean) exhibit rate comodulation, whereas ideal coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization. … Our results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate comodulation can encode different stimulus properties. Our results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons. “
1 . Hong S, Ratte S, Prescott SA, De Schutter E (2012) Single neuron firing properties impact correlation-based population coding. J Neurosci 32:1413-28 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Generic;
Cell Type(s): Hodgkin-Huxley neuron; Abstract Morris-Lecar neuron;
Gap Junctions:
Simulation Environment: NEURON (web link to model); Python (web link to model);
Model Concept(s): Synchronization; Noise Sensitivity;
Implementer(s): Hong, Sungho [shhong at oist.jp];
(located via links below)
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